- Overplan during study startup.
- Get the whole trial execution team, including data management and stats, together around the table in the beginning.
- Do a dry run of the interim analysis, with everybody around the table. Personally, I think it's worth it to fly people in if they are scattered around the world, but at the very least use the web conferencing technologies.
- Draw a diagram of data flow for the interim analysis. Use Visio, a white board, note cards and string, or whatever is useful. The process of making this diagram is more important than the diagram itself, but the diagram is important as well. Of course, this process will more than likely change during the course of the study but these diagrams can be updated as well.
- Fuss over details. Little details can trip up the team when the chips are down. Make the process as idiot-proof as possible. I once had a situation where I screwed up an interim analysis because I forgot to change the randomization directory from a dummy randomization (so blinded programmers to write programs) to the real randomization (so I could produce the reports). After that, I talked with the lead programmer and refined the report production process even further.
- Plan for turnover. You like members of your team, and some of them will go away during the execution of the trial. New members will come on board. Business continuity planning is very important and is increasingly being scrutinized. Scrutinize it on your trials. Because you've overplanned, done some dry runs, drawn diagrams, and fussed over details, you've written all these down, so the content's readily available to put together in a binder (or pdf). You might even repeat the dry run process with new staff.
- And, for the statisticians, run clinical trial simulations. A well-done simulation will not only show how the trial performs, but also illuminate the assumptions behind the trial. Then simulations can be performed to show how robust the trial is regarding those assumptions.
Wednesday, September 15, 2010
Clinical trials are hard enough to do as it is, because many people coming from many different backgrounds and having many different focuses have to coordinate their efforts to make a good quality finished product--a clinical trial with good data that answers the research questions in a persuasive and scientifically valid way. Add to that mix several interim analyses with tight turnaround times (required to make the interim analysis results useful enough to adapt the trial) and you really are putting your sites, clinical, data management, and statistical teams in the pressure cooker. Making stupid mistakes that your teams would not ordinarily make is a real danger (believe me, it is and I have made a few of those myself), and one that can endanger the results of the interim analysis. Here are some ideas to cut down on those stupid mistakes: